Public articles linked to the same research event.
arXiv Using 6874 molecules selected from the QM7 dataset, with ground-state densities computed by DFT and absorption spectra by linear-response TDDFT, this work compares a 3D convolutional neural network taking volumetric electron density as input against graph neural networks (SchNet, DimeNet++, MACE) taking atom-centred geometry as input for predicting normalized absorption spectra, finding that the density CNN achieves a validation correlation of 0.9926 versus 0.9795 for the best geometry-based graph model, reducing residual decorrelation by about two-thirds.
Using 6874 molecules selected from the QM7 dataset, with ground-state densities computed by DFT and absorption spectra by linear-response TDDFT, this work compares a 3D convolutional neural network taking volumetric electron density as input against graph neural networks (SchNet, DimeNet++, MACE) taking atom-centred geometry as input for predicting normalized absorption spectra, finding that the density CNN achieves a validation correlation of 0.9926 versus 0.9795 for the best geometry-based graph model, reducing residual decorrelation by about two-thirds.
Using 6874 molecules selected from the QM7 dataset, with ground-state densities computed by DFT and absorption spectra by linear-response TDDFT, this work compares a 3D convolutional neural network taking volumetric electron density as input against graph neural networks (SchNet, DimeNet++, MACE) taking atom-centred geometry as input for predicting normalized absorption spectra, finding that the density CNN achieves a validation correlation of 0.9926 versus 0.9795 for the best geometry-based graph model, reducing residual decorrelation by about two-thirds.
Using 6874 molecules selected from the QM7 dataset, with ground-state densities computed by DFT and absorption spectra by linear-response TDDFT, this work compares a 3D convolutional neural network taking volumetric electron density as input against graph neural networks (SchNet, DimeNet++, MACE) taking atom-centred geometry as input for predicting normalized absorption spectra, finding that the density CNN achieves a validation correlation of 0.9926 versus 0.9795 for the best geometry-based graph model, reducing residual decorrelation by about two-thirds.